Inferential Statistics

Inferential Statistics

Inferential Statistics

Duke University

About this course: This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course!

2 readings

Leyendo: About Statistics with R Specialization

Leyendo: More about Inferential Statistics

Central Limit Theorem and Confidence Interval

Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts, you will also see a few videos that walk you through application examples related to the week's topics. In the first week we will introduce Central Limit Theorem (CLT) and confidence interval.

7 videos, 4 readings, 1 practice quiz

Leyendo: Lesson Learning Objectives

Video: Introduction

Video: Sampling Variability and CLT

Video: CLT (for the mean) examples

Leyendo: Lesson Learning Objectives

Video: Confidence Interval (for a mean)

Video: Accuracy vs. Precision

Video: Required Sample Size for ME

Video: CI (for the mean) examples

Leyendo: Week 1 Suggested Readings and Practice Exercises

Cuestionario de práctica: Week 1 Practice Quiz

Leyendo: Week 1 Lab Instructions

Graded: Week 1 Quiz

Graded: Week 1 Lab

WEEK 2

Inference and Significance

Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.

7 videos, 4 readings, 1 practice quiz

Leyendo: Lesson Learning Objectives

Video: Another Introduction to Inference

Video: Hypothesis Testing (for a mean)

Video: HT (for the mean) examples

Leyendo: Lesson Learning Objectives

Video: Inference for Other Estimators

Video: Decision Errors

Video: Significance vs. Confidence Level

Video: Statistical vs. Practical Significance

Leyendo: Week 2 Suggested Readings and Practice Exercises

Cuestionario de práctica: Week 2 Practice Quiz

Leyendo: Week 2 Lab Instructions

Graded: Week 2 Quiz

Graded: Week 2 Lab

WEEK 3

Inference for Comparing Means

Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum to ask/discuss with peers.

11 videos, 4 readings, 1 practice quiz

Leyendo: Lesson Learning Objectives

Video: Introduction

Video: t-distribution

Video: Inference for a mean

Video: Inference for comparing two independent means

Video: Inference for comparing two paired means

Video: Power

Leyendo: Lesson Learning Objectives

Video: Comparing more than two means

Video: ANOVA

Video: Conditions for ANOVA

Video: Multiple comparisons

Video: Bootstrapping

Leyendo: Week 3 Suggested Readings and Practice Exercises

Cuestionario de práctica: Week 3 Practice Quiz

Leyendo: Week 3 Lab Instructions

Graded: Week 3 Quiz

Graded: Week 3 Lab

WEEK 4

Inference for Proportions

Welcome to Week Four of our course! In this unit, we’ll discuss inference for categorical data. We use methods introduced this week to answer questions like “What proportion of the American public approves of the job of the Supreme Court is doing?”.

11 videos, 4 readings, 1 practice quiz

Leyendo: Lesson Learning Objectives

Video: Introduction

Video: Sampling Variability and CLT for Proportions

Video: Confidence Interval for a Proportion

Video: Hypothesis Test for a Proportion

Video: Estimating the Difference Between Two Proportions

Video: Hypothesis Test for Comparing Two Proportions

Leyendo: Lesson Learning Objectives

Video: Small Sample Proportions

Video: Examples

Video: Comparing Two Small Sample Proportions

Video: Chi-Square GOF Test

Video: The Chi-Square Independence Test

Leyendo: Week 4 Suggested Readings and Practice Exercises

Cuestionario de práctica: Week 4 Practice Quiz

Leyendo: Week 4 Lab Instructions

Graded: Week 4 Quiz

Graded: Week 4 Lab

WEEK 5

Data Analysis Project

In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

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Recibe una calificación final

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Ratings and Reviews

Rated 4.8 out of 5 of 636 ratings

ET

Very interesting, good pace.

BF

Great foundational class in stats analysis -- would like more time spent on ANOVA and some of the non-T test related analyses as these felt a bit brushed over

AG

Excelent !!!

another wonderful course and perfect if you followed the intro course.